Wind power is considered one of the cleanest, most environmentally friendly energy sources presently available, and wind turbines have gained increased attention in this regard. A modern wind turbine typically includes a tower, generator, gearbox, nacelle, and one or more rotor blades. The rotor blades capture kinetic energy of wind using known airfoil principles. For example, rotor blades typically have the cross-sectional profile of an airfoil such that, during operation, air flows over the blade producing a pressure difference between the sides. Consequently, a lift force, which is directed from a pressure side towards a suction side, acts on the blade. The lift force generates torque on the main rotor shaft, which is geared to a generator for producing electricity.
A plurality of wind turbines are commonly used in conjunction with one another to generate electricity and are commonly referred to as a “wind farm.” Wind turbines on a wind farm typically include their own meteorological monitors that perform, for example, temperature, wind speed, wind direction, barometric pressure, and/or air density measurements. In addition, a separate meteorological mast or tower (“met mast”) having higher quality meteorological instruments that can provide more accurate measurements at one point in the farm may also be provided. The correlation of meteorological data with power output provides the empirical determination of a “power curve” for the individual wind turbines.
Typically, in a wind farm, each wind turbine attempts to maximize its own power output while maintaining its fatigue loads within desirable limits. To this end, each turbine includes a control module, which attempts to maximize power output of the turbine in the face of varying wind and grid conditions, while satisfying constraints like sub-system ratings and component loads. Based on the determined maximum power output, the control module controls the operation of various turbine components, such as the generator/power converter, the pitch system, the brakes, and the yaw mechanism to reach the maximum power efficiency.
Often, while maximizing the power output of a single wind turbine, neighboring turbines may be negatively impacted. For example, downwind turbines may experience large wake effects caused by an upwind turbine. Wake effects include reduction in wind speed and increased wind turbulence downwind from a wind turbine typically caused by the conventional operation of upwind turbines (i.e. for maximum power output). Because of these wake effects, downwind turbines receive wind at a lower speed, drastically affecting their power output (as power output is proportional to wind speed). Moreover, wind turbulence negatively affects the fatigue loads placed on the downwind turbines, and thereby affects their life (as life is proportional to fatigue loads). Consequently, maximum efficiency of a few wind turbines may lead to sub-optimal power output, performance, or longevity of other wind turbines in the wind farm. Thus, modern control technologies attempt to optimize the wind farm power output rather than the power outputs of each individual wind turbine.
In addition, there are many products, features, and/or upgrades available for wind turbines and/or wind farms so as to increase power output or annual energy production (AEP) of the wind farm. Once an upgrade has been installed, it is advantageous to efficiently determine various wind turbine performance improvement measurements so as to verify the benefit of the upgrade. For example, a typical method for assessing wind turbine performance measurements is to baseline power against wind speed as assessed by the turbine nacelle anemometer. The nacelle anemometer approach, however, is sometimes hindered due to imprecision of nacelle anemometer measurements and the projection of these measurements into AEP estimates. Further, such an approach may be less preferred than use of an external met mast in front of a wind turbine, but is in widespread use due to the generally prohibitive cost of the met mast approach. In addition, even when nacelle anemometers are calibrated correctly, individual wind power curve methods are not able to discern the benefit of upgrades, such as wake minimization technologies, that can create more wind for the farm to use. In view of the aforementioned issues, still another approach for assessing wind turbine performance measurements is to baseline performance against a control turbine in close proximity to a turbine of interest. Such an approach, however, is subject to the availability of the control turbine and further inaccuracies due to reliance on a single, more distant sensor. Thus, it is difficult to show the benefit of upgrades to individual turbines.
Accordingly, there is a need for improved systems and methods for validating wind farm performance improvement measurements that address the aforementioned issues. Thus, the present disclosure is directed to systems and methods for baselining wind turbine performance measurements using multi-feature estimation that normalizes AEP uncertainty estimates.